Independent DePIN data and mathematical research

Kairos Signal is an independent data and research operation focused on making DePIN measurements easier to inspect and use. The commercial data products organize supported network, blockchain, and market observations for research workflows; derived analysis identifies its inputs and limitations. Experimental mathematical and machine-learning work — including the 63-layer Neural SDE (ByteDAG) signal research on 47 crypto perpetual markets — is presented separately, with its own implementation status and evaluation evidence. The entire system runs on self-owned infrastructure. No cloud, no VC.

What It Does

ByteDAG runs a 256-to-256 forward pass with a 512-wide whiteboard feature space. The vector the API actually returns is 32 values. The model ingests raw market tick data stored in ClickHouse and produces directional signals for 47 crypto perpetual futures contracts on Hyperliquid.

No historical performance or return figure is claimed, and no backtest, win-rate or return figure is published.

The architecture is informed by peer-reviewed mathematical sources spanning stochastic calculus, gauge theory, and topological data analysis. The layers are implemented in PyTorch, trained on real data, and tested empirically.

The 63-Layer Architecture

All raw tick data feeds into the ByteDAG encoder. What executes, read from the code rather than the design document:

• All 63 layers execute over a 256-wide state (191-edge topology; 62 of the 63 nodes are invoked each pass — node 0 is the input state itself, seeded rather than computed). Layer names such as “Neural SDE”, “Lyapunov” and “Green’s function” label what each block is meant to approximate; they are learned linear maps with nonlinearities, not separate solvers. The published vector draws on the 42 layers in the terminal layer’s ancestry; 21 additional layers compute but do not feed it under the current single-head loss.
• Gauge head (layer 62): computes a Wilson-loop holonomy W from the log-covariance; W has measured identically 1.0 in production and is discarded, so it does not shape the output today.
• Published vector: the first 32 dimensions of the final state. Layers 51–52 draw unseeded noise, so the vector is not bit-reproducible across calls.

The 5.77M-parameter weights, the 63-layer thesis design and the separate symplectic prototype are three different objects; this page describes the one that serves requests.

By the Numbers

• 63 layers in the ByteDAG neural SDE
• 256-to-256 internal forward pass (the published vector is 32 values)
• 512-wide internal whiteboard feature space
• 47 crypto perpetual futures covered
• Raw market tick data in ClickHouse
• 1,902 blog posts published
• Peer-reviewed mathematical sources referenced
(no performance figure claimed)

What It Is Not

This is not investment advice. Kairos Signal is not a broker-dealer, investment advisor, or financial services provider. It is a machine learning research product. All outputs are for research and informational purposes only.